Abstract: Semi-supervised learning methods can significantly improve the generalization ability of models while reducing labeling costs by utilizing a small amount of labeled data together with a large amount of unlabeled data.However,most existing methods rely on confidence thresholds to select unlabeled samples,causing some samples to remain underutilized in the later stages of training,which leads to information waste and limits further performance improvement.In addition,hard samples often have a negative impact on the training process,resulting in reduced model robustness.To address these issues,this paper proposes a semi-supervised learning method enhanced with prototype loss.By designing a dual-head training framework,the proposed method fully exploits the latent information in unlabeled data and effectively mitigates the negative impact of hard samples on model performance.Specifically,it employs consistency regularization to enforce prediction consistency for unlabeled samples under different perturbations,and introduces a prototype-based surrogate loss that measures the similarity between sample features and class prototypes to guide unlabeled samples toward the correct categories.Experimental results show that the proposed me-thod achieves significant performance improvements on multiple datasets,fully validating its effectiveness and robustness.In particular,on the CIFAR-10 and CIFAR-100 datasets with only 4 labeled samples per class,the proposed method improves classification accuracy by 1.82 percentage points and 1.07 percentage points compared to the best baseline methods,respectively.

Key words: Semi-supervised learning, Feature prototype, Dual-head training framework, Information mining, Consistency regularization